Sakura-EmbeddingGemma-2-HighQuality requirements — can you run it?
· 5 quantizations measured · updated 2026-10-08 · source: webmp3/Sakura-EmbeddingGemma-2-HighQuality-GGUF
Sakura-EmbeddingGemma-2-HighQuality is a transformer model (1k downloads on Hugging Face), published as GGUF by the community. At the recommended Q4 quant it needs 1.7 GB of VRAM or system RAM at 8K context. Builds measured on this page range from 1.7 GB (smallest quant) to 1.8 GB (lossless). Every number below comes from the actual GGUF files - not a params x bits formula.
# llama.cpp (auto-downloads the GGUF) llama-cli -hf webmp3/Sakura-EmbeddingGemma-2-HighQuality-GGUF:Q4 # Ollama (pulls straight from Hugging Face) ollama run hf.co/webmp3/Sakura-EmbeddingGemma-2-HighQuality-GGUF:Q4 # LM Studio: search "webmp3/Sakura-EmbeddingGemma-2-HighQuality-GGUF" in the model browser
Commands load the exact quant measured on this page.
Every quantization: real file size vs what you actually need
| Quant | File (measured) | VRAM/RAM needed (8K ctx) | Runs on (first fit) |
|---|---|---|---|
| Q4 | 0.2 GB | 1.7 GB | RTX 3060 12GB |
| Q4-L | 0.2 GB | 1.7 GB | RTX 3060 12GB |
| Q5-S | 0.2 GB | 1.7 GB | RTX 3060 12GB |
| Q5 | 0.2 GB | 1.8 GB | RTX 3060 12GB |
| Q6 | 0.2 GB | 1.8 GB | RTX 3060 12GB |
KV cache uses a flat +20% context/overhead rule because this repo does not publish its config; exact numbers may differ for long contexts. MoE models keep all experts in memory — total size counts, not just active params. Longer context grows the KV cache — use the selector above.
Can I run Sakura-EmbeddingGemma-2-HighQuality on my GPU? (Q4 recommended quant)
| Hardware | Memory | Verdict | Est. speed |
|---|---|---|---|
| RTX 3060 12GB | 12 GB | RUNS | ~1525 tok/s |
| RTX 4060 Ti 16GB | 16 GB | RUNS | ~1220 tok/s |
| RTX 3090 24GB | 24 GB | RUNS | ~3965 tok/s |
| RTX 4090 24GB | 24 GB | RUNS | ~4270 tok/s |
| RTX 5090 32GB | 32 GB | RUNS | ~7591 tok/s |
| RTX PRO 6000 96GB | 96 GB | RUNS | ~7591 tok/s |
| Mac 16GB unified | 16 GB | RUNS | ~424 tok/s |
| Mac 32GB unified | 32 GB | RUNS | ~635 tok/s |
| Mac 64GB unified | 64 GB | RUNS | ~1156 tok/s |
| Mac 128GB unified | 128 GB | RUNS | ~1694 tok/s |
| Mac 256GB unified | 256 GB | RUNS | ~2313 tok/s |
| Mac 512GB unified | 512 GB | RUNS | ~3469 tok/s |
| 32GB system RAM (CPU) | 32 GB | RUNS | ~212 tok/s |
| 64GB system RAM (CPU) | 64 GB | RUNS | ~212 tok/s |
| 128GB system RAM (CPU) | 128 GB | RUNS | ~339 tok/s |
| 256GB system RAM (CPU) | 256 GB | RUNS | ~339 tok/s |
| 1TB server RAM (CPU) | 1024 GB | RUNS | ~847 tok/s |
| 2TB server RAM (CPU) | 2048 GB | RUNS | ~847 tok/s |
Speed = decode tok/s estimated from memory bandwidth ÷ measured file size (× MoE active share). Real numbers vary ±30% by runtime and settings.
How much VRAM does Sakura-EmbeddingGemma-2-HighQuality need?
The honest answer: 1.7 GB at the Q4 quant with 8K context — that is the measured
file size (0.2 GB) plus KV cache and runtime overhead. The smallest published build needs
1.7 GB; the lossless one needs 1.8 GB.
Cheapest hardware that runs it: Used RTX 3070 8GB (~$220, used market) — see which GPU to buy.
Can I run it with CPU offload?
Yes, if combined RAM+VRAM ≥ file size — but generation speed is limited by memory bandwidth. Expect roughly: DDR5 dual channel ~10-30 tok/s for small MoE actives, single digits for big ones, 0.1-1 tok/s when experts page from disk. CPU-only is fine for batch jobs, painful for chat.
Why do other sites show different numbers?
Most pages compute weights from a formula (params × bits/8). We use the actual file sizes published in GGUF repos, which include the embedding table, unquantized tensors, and container overhead — that is why our numbers can differ from a naive calculation.
FAQ
How much VRAM does Sakura-EmbeddingGemma-2-HighQuality need?
The measured answer: 1.7 GB at the Q4 quant with 8K context. The smallest published build needs 1.7 GB; the lossless (F16/BF16) build needs 1.8 GB. These are real GGUF file sizes from Hugging Face, not formula estimates.
Can I run Sakura-EmbeddingGemma-2-HighQuality on a 24GB GPU (RTX 3090/4090)?
Yes — at Q4 (1.7 GB). Quants that fit 24GB: Q4, Q4-L, Q5-S, Q5, Q6.
How fast will Sakura-EmbeddingGemma-2-HighQuality run?
Decode speed is memory-bandwidth-bound. At Q4: roughly 4270 tok/s on an RTX 4090, 3965 tok/s on an RTX 3090, 1694 tok/s on an M-series Mac with 128GB, and 211.8 tok/s on CPU with dual-channel DDR4 — estimates from measured file size, MoE active-parameter share and memory bandwidth.
Can I run Sakura-EmbeddingGemma-2-HighQuality on CPU with system RAM?
Yes, if your usable RAM is at least the file size. At Q4 you need ~1.7 GB of RAM. CPU generation is memory-bandwidth-bound: expect single-digit tok/s for large MoE models, 10-30 tok/s for small active params, and below 1 tok/s when experts page from disk.
Why do VRAM numbers for Sakura-EmbeddingGemma-2-HighQuality differ between sites?
Most sites compute weights from a formula (params x bits/8). ModelFit uses the actual GGUF file sizes published on Hugging Face, which include the embedding table, unquantized tensors, and container overhead — so our numbers reflect what you really download and load.